Hi Peng,
You are right about the 1st two. If it is hard to find out from the
wet-lab folks which version of the platform they are running, the probe
IDs in their output should help you figure out which version you have.
There are several methods you can use to get the IDs mapped onto the
probes. These are described in the vignettes for the AnnotationDbi,
annotate and annaffy package. You can look at the vignettes for these
packages by browsing our web site, or by loading the package and using
openVignette(). What you do here will really depend on what you have
and what you want to get.
Marc
Peng Yu wrote:
> Hi Marc,
>> I am using Affymetrix Mouse Gene 1.0-ST Array. I should ask the person
> who did the experiment which version the array is, right? Base on the
> version, I should use either mogene10stprobeset.db or
> mogene10sttranscriptcluster.db, right?
>> I have read the document, but I am still not sure what command I
> should use to convert the id to the annotation. Suppose I should use
> mogene10stprobeset.db, would you please let me know what commands I
> should use?
>> Regards,
> Peng
>> On Tue, Jul 28, 2009 at 9:56 AM, Marc Carlson<mcarlson at fhcrc.org> wrote:
>>> Hi Peng,
>>>> What platform are you using? You can probably find an annotation
>> package here that will contain all three types of annotation requested.:
>>>>http://www.bioconductor.org/packages/release/data/annotation/>>>> If not you can make a chip package that will meet you needs by following
>> the instructions here:
>>>>http://www.bioconductor.org/packages/release/bioc/html/AnnotationDbi.html>>>>>>>> Marc
>>>>>>>>>> Peng Yu wrote:
>>>>> Hi,
>>>>>> I use the following code to normalized the data. It seems that the
>>> data file has the Affimetrix id. I would like to includes MGI gene
>>> symbol, Entrez gene symbol, gene description in the output file. I am
>>> wondering if there is a way to do so with BioC. What command I should
>>> use?
>>>>>> Regards,
>>> Peng
>>>>>> library(oligo)
>>> data<-read.celfiles(list.celfiles())
>>> eset<-rma(dat)
>>> eset<-rma(data)
>>> write.exprs(eset, file="output.txt", sep="\t")
>>>>>> The data look like the following.
>>>>>> koA-mth_HZ_5238_MST1_19389.cel koB-mth_HZ_5238_MST1_19390.cel
>>> koC-mth_HZ_5238_MST1_19391.cel koD-mth_HZ_5238_MST1_19392.cel
>>> wt1-mth_HZ_5238_MST1_19385.cel wt2-mth_HZ_5238_MST1_19386.cel
>>> wt3-mth_HZ_5238_MST1_19387.cel wt4-mth_HZ_5238_MST1_19388.cel
>>> 10344615 7.07210987006919 7.01089258722033
>>> 7.26426270000726.92980486555595 7.72857978063884
>>> 6.91124431275741 7.457761829613277.21025349865986
>>> 10344617 3.02519545040591 3.08697023169755
>>> 3.032032340858283.09846420636071 3.12487891156704
>>> 3.10727683101607 3.0544609560487 3.03353963677405
>>> 10344619 3.20294677833793 3.20612630466463
>>> 3.176553031536723.13210443165341 3.1378507207366
>>> 3.21452663497659 3.313450502242243.09287042099817
>>>>>> _______________________________________________
>>> Bioconductor mailing list
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> Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch>https://stat.ethz.ch/mailman/listinfo/bioconductor> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor>>